CN109215034A - A kind of Weakly supervised image, semantic dividing method for covering pond based on spatial pyramid - Google Patents
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Abstract
The invention discloses a kind of Weakly supervised image, semantic dividing methods that pond is covered based on spatial pyramid, comprising the following steps: selectes a convolutional neural networks H, handles input picture X by convolutional neural networks H, obtain characteristic of division figure;Spatial pyramid pond module is established according to characteristic of division figure, then carries out spatial pyramid cover, obtains output characteristic pattern;Classification is calculated according to output characteristic pattern and activates vector sum class probability vector, is then established competitive spatial pyramid and is covered pond loss function;Pond loss function training convolutional neural networks H is covered according to competitive spatial pyramid and extracts segmentation characteristic pattern.The present invention realizes richer local feature, the provincial characteristics more perfect and target sizes of excavation and the more robust Weakly supervised image, semantic parted pattern of posture, the extractability for improving local semantic information strengthens localized target or the recognition capability at position in Weakly supervised semantic segmentation.
Description
Technical field
The invention belongs to technical field of computer vision, and in particular to a kind of weak prison that pond is covered based on spatial pyramid
Superintend and direct image, semantic dividing method.
Background technique
Image, semantic is divided as basic Computer Vision Task, and target is to be divided all pixels in image
Class.Since it can be fully understood by image in Pixel-level, so all for other visual tasks such as image classification and target identification
There is booster action.But because the production of Pixel-level label data needs to expend great effort, the image, semantic of full monitor mode
Segmentation is difficult to fast implement extensive extension, and the Weakly supervised image, semantic dividing method for then relying on image class label is extensive
Research.
In past computer vision research, there are many pyramid model application.Famous SIFT algorithm passes through La Pula
This pyramid extracts key point Expressive Features, becomes a kind of detective operators with Scale invariant property.Later, pyramid model
It is successfully applied to convolutional neural networks.(spatial pyramid pooling, SPP) be used to roll up in spatial pyramid pond
Local features are extracted in product neural network tail portion, and SPP obtains preferably effect in image classification and target identification.Cavity
Spatial pyramid (atrous spatial pyramid pooling, ASPP) module is based on multiple dispersion ratio (dilated
Rate empty convolution (dilated convolution)) obtains pyramid feature, and is applied to image, semantic and divides.Golden word
Characteristic pattern is sliced into different spaces and explores part and whole semantic relation by tower scene cut model, by entirety and part
The semantic information in region compares and combines to obtain more robust segmentation result.
There are no relatively successfully applications in Weakly supervised semantic segmentation for pyramid model.On the one hand, with global pool knot
The sorter network of tail is only capable of obtaining target-recognition region due to learning in a manner of Weakly supervised, lacks mentioning for local region information
Ability is taken, the shortcomings that pyramid model obviously can make up global pool due to the characteristic of its own.On the other hand, pyramid mould
Although type has the characteristics of multi-scale information and local message merge, but how preferably on the basis of grasping global information
The problem that time important semantic information is excavated in subregion is fully solved not yet.
Summary of the invention
It is an object of the invention to: above-mentioned deficiency in the prior art is solved, is provided a kind of based on spatial pyramid cover
The Weakly supervised image, semantic dividing method in pond, improves the extractability of local semantic information, strengthens Weakly supervised semantic point
Cut the recognition capability at middle localized target or position.
To achieve the goals above, the technical solution adopted by the present invention are as follows:
A kind of Weakly supervised image, semantic dividing method for covering pond based on spatial pyramid, comprising the following steps:
Step 1: selecting a convolutional neural networks H, handles input picture X by convolutional neural networks H, is classified
Characteristic pattern;
Step 2: establishing spatial pyramid pond module according to characteristic of division figure, then carries out spatial pyramid cover, obtains
To output characteristic pattern;
Step 3: calculating classification according to output characteristic pattern and activate vector sum class probability vector, then establishes competitive empty
Between pyramid cover pond loss function;
Step 4: it covers pond loss function training convolutional neural networks H according to competitive spatial pyramid and extracts and divide
Cut characteristic pattern.
Further, the step of input picture X being handled by convolutional neural networks H in above-mentioned steps one specifically:
Step 101: selecting a convolutional neural networks H, map to obtain segmentation characteristic pattern by the convolutional neural networks HWherein, C is target category quantity, and W is weight parameter, For real number field;
Step 102: characteristic pattern dimensionality reduction will be divided by 1 × 1 convolution, obtain characteristic of division figureIt calculates public
Formula are as follows:
Fcls=Fseg×W+b
Wherein, b is biasing,
Further, above-mentioned step two specifically:
Step 201: average pond being carried out to all characteristic of division figures, while specifying total pyramid number and Chi Huahe big
It is small, obtain corresponding pyramid pond characteristic pattern Pi, wherein i is pyramid serial number, and value range arrives total pyramid number between 1
Between mesh, i-th of pyramid pond characteristic pattern is calculatedCalculation formula are as follows:
Wherein, kiFor i-th of pyramidal pond core size, x is characterized figure abscissa, and y is characterized figure ordinate, △ kx
For pond core abscissa, △ kyFor pond core ordinate, c is semantic classes quantity;
Step 202: each pyramid pond characteristic pattern being generated and covers tensorRegion is concurrently set to cover
Lid ratio and cover quantization parameter;
Step 203: according to the output characteristic pattern for covering tensor computation spatial pyramid cover pondMeter
Calculate formula are as follows:
Oi=Pi⊙Mi
Wherein, ⊙ indicates hadamard product.
Further, in above-mentioned steps 202, remember that i-th of pyramid covers tensor and beRemember jth eka-gold
Word tower covers tensor, the region that the different classes of pyramid covers tensor covers position and covers quantization
Coefficient is consistent:
The ratio that the cover region generated at random accounts for the pyramid pond number of regions is τ,
Wherein, the value range of τ is [0,1], and the value of τ is bigger, and the region for indicating to cover is more,It indicates
(1-τ)HiThe value that W is obtained is rounded downwards, and I () indicates that indicator function, I () take 1 when input condition meets, other situations
Under take 0, i.e.,The position of satisfaction is to be retained region, and other situations are the region covered;
The cover quantization parameter ρ is for inhibiting signal;
Wherein, the value range of ρ is [0,1].
Further, classification is calculated in above-mentioned steps three activate vector sum class probability vector specifically:
Step 301: calculating classification and activate vector oi, i.e., the output characteristic pattern that spatial pyramid covers pond is classified as one
Value, calculation formula are as follows:
Step 302: class probability vector being calculated by Softmax function, obtains the probability of each semantic classes to be determined
Value, calculation formula are as follows:
Further, in above-mentioned steps three, establish competitive spatial pyramid cover pond loss function specifically include with
Lower step:
Step 303: calculating the Classification Loss l of each pyramid gradei, calculation formula are as follows:
Wherein,1 other places are taken to take 0, T for image, semantic class label vector and at the corresponding semantic classes target of appearance
For transposition symbol;
Step 304: the 0th grade of global pool information being fully retained, other pyramids for having carried out information cover etc. are grading
Mobile state competition, calculation formula are as follows:
Wherein, lclsFor loss function of always classifying, N is pyramid number of levels, l0For the damage of global pool pyramid grade
It loses;
Further, above-mentioned step four specifically: error is calculated with loss function of always classifying and is calculated by backpropagation
Method adjusts network parameter after the opposite variation of the loss function is less than 5%, directly by FsegIt takes out and is taken in classification dimension
Prediction segmentation figure can be obtained in argmax
Y=argmax (Fseg)
Wherein, argmax FsegThe 3rd dimension.
By adopting the above-described technical solution, the beneficial effects of the present invention are:
It is more robust that the present invention realizes richer local feature, the provincial characteristics more perfect and target sizes of excavation and posture
Weakly supervised image, semantic parted pattern.
The present invention obtains finer segmentation result to preferably excavate local message, to spatial pyramid pond module
Increase cover mechanism, in addition to the diversity of extension feature figure, identification region can also be inhibited and then encourage identification more times
The study in region;
The case where misplacing just present invention is alternatively directed to different pyramidal cover regions, proposes that auxiliary space pyramid is covered
The competitive spatial pyramid of pondization training covers pond loss function, reduces the risk of network training failure;
Global pool information of the invention make network to the scale of target will not tetchiness, and spatial pyramid is covered
Gai Chihua more has versatility in other visual tasks.
Detailed description of the invention
Fig. 1 is the Weakly supervised image, semantic parted pattern schematic diagram of the invention that pond is covered based on spatial pyramid.
Fig. 2 is that spatial pyramid of the invention covers pond structural schematic diagram.
Fig. 3 is that the Weakly supervised image, semantic of the invention for covering pond based on spatial pyramid divides flow diagram.
Fig. 4 is segmentation result contrast schematic diagram of the invention.
Specific embodiment
Referring to attached drawing 1-4, specific description is done to embodiments of the present invention.
A kind of Weakly supervised image, semantic dividing method for covering pond based on spatial pyramid, comprising the following steps:
Step 1: selecting a convolutional neural networks H, handles input picture X by convolutional neural networks H, is classified
Characteristic pattern;
Step 2: establishing spatial pyramid pond module according to characteristic of division figure, then carries out spatial pyramid cover, obtains
To output characteristic pattern;
Step 3: calculating classification according to output characteristic pattern and activate vector sum class probability vector, then establishes competitive empty
Between pyramid cover pond loss function;
Step 4: it covers pond loss function training convolutional neural networks H according to competitive spatial pyramid and extracts and divide
Cut characteristic pattern.
Further, the step of input picture X being handled by convolutional neural networks H in above-mentioned steps one specifically:
Step 101: selecting a convolutional neural networks H, map to obtain segmentation characteristic pattern by the convolutional neural networks HWherein, C is target category quantity, and W is weight parameter, For real number field;
Step 102: characteristic pattern dimensionality reduction will be divided by 1 × 1 convolution, obtain characteristic of division figureIt calculates public
Formula are as follows:
Fcls=Fseg×W+b
Wherein, b is biasing,
Further, above-mentioned step two specifically:
Step 201: average pond being carried out to all characteristic of division figures, while specifying total pyramid number and Chi Huahe big
It is small, obtain corresponding pyramid pond characteristic pattern Pi, wherein i is pyramid serial number, and value range arrives total pyramid number between 1
Between mesh, i-th of pyramid pond characteristic pattern is calculatedCalculation formula are as follows:
Wherein, kiFor i-th of pyramidal pond core size, x is characterized figure abscissa, and y is characterized figure ordinate, △ kx
For pond core abscissa, △ kyFor pond core ordinate, c is semantic classes quantity;
Step 202: each pyramid pond characteristic pattern being generated and covers tensorRegion is concurrently set to cover
Lid ratio and cover quantization parameter;
Step 203: according to the output characteristic pattern for covering tensor computation spatial pyramid cover pondMeter
Calculate formula are as follows:
Oi=Pi⊙Mi
Wherein, ⊙ indicates hadamard product.
Further, in above-mentioned steps 202, remember that i-th of pyramid covers tensor and beRemember jth eka-gold
Word tower covers tensor, the region that the different classes of pyramid covers tensor covers position and covers quantization
Coefficient is consistent:
The ratio that the cover region generated at random accounts for the pyramid pond number of regions is τ,
Wherein, the value range of τ is [0,1], and the value of τ is bigger, and the region for indicating to cover is more,It indicates
(1-τ)HiThe value that W is obtained is rounded downwards, and I () indicates that indicator function, I () take 1 when input condition meets, other situations
Under take 0, i.e.,The position of satisfaction is to be retained region, and other situations are the region covered;
The cover quantization parameter ρ is for inhibiting signal;
Wherein, the value range of ρ is [0,1].
Further, classification is calculated in above-mentioned steps three activate vector sum class probability vector specifically:
Step 301: calculating classification and activate vector oi, i.e., the output characteristic pattern that spatial pyramid covers pond is classified as one
Value, calculation formula are as follows:
Step 302: class probability vector being calculated by Softmax function, obtains the probability of each semantic classes to be determined
Value, calculation formula are as follows:
Further, in above-mentioned steps three, establish competitive spatial pyramid cover pond loss function specifically include with
Lower step:
Step 303: calculating the Classification Loss l of each pyramid gradei, calculation formula are as follows:
Wherein,1 other places are taken to take 0, T for image, semantic class label vector and at the corresponding semantic classes target of appearance
For transposition symbol;
Step 304: the 0th grade of global pool information being fully retained, other pyramids for having carried out information cover etc. are grading
Mobile state competition, calculation formula are as follows:
Wherein, lclsFor loss function of always classifying, N is pyramid number of levels, l0For the damage of global pool pyramid grade
It loses;
Further, above-mentioned step four specifically: error is calculated with loss function of always classifying and is calculated by backpropagation
Method adjusts network parameter after the opposite variation of the loss function is less than 5%, directly by FsegIt takes out and is taken in classification dimension
Prediction segmentation figure can be obtained in argmax
Y=argmax (Fseg)
Wherein, argmax FsegThe 3rd dimension.
The present embodiment verifies the validity of the method for the invention by Comparative result that image, semantic is divided.
Step 1 chooses DeepLab v2 network as depth characteristic extraction model, input pictureSource
In PASCAL VOC semantic segmentation data set, the segmentation characteristic pattern handled by H
Step 2, segmentation characteristic pattern obtain characteristic of division figure by 1 × 1 convolution
Characteristic of division figure is carried out spatial pyramid pond by step 3, and pond core size is respectively 40,20,10,8 and 5,
Obtain corresponding pyramid pond characteristic pattern With
Step 4 generates and covers tensor.It is 0 that ratio τ is covered in 0th grade of global pool region, and other pyramid hierarchical regions are covered
Lid ratio τ and cover quantization parameter ρ is respectively 0.25 and 0.0.Tensor is so covered to meet:
The output characteristic pattern that each pyramid grade covers pond is calculated in step 5
Step 6 calculates classification activation vector:
Step 7: calculating class probability vector
Step 8, the intersection entropy loss for calculating each hierarchical pyramid,
Step 9 calculates competitive spatial pyramid cover pond loss function,
Step 10 repeats step 1 to step 9 progress network training to the more image samples of convolutional neural networks input, leads to
Competitive spatial pyramid is crossed to cover pond loss function reverse propagated error and then update network.When test, one is inputted newly
Image and extract segmentation characteristic patternPrediction segmentation figure Y is obtained after seeking argmax again.
It is noted that the setting of ratio τ and cover quantization parameter ρ that input image resolution, region are covered specifically are being flowed
It is an example in journey, choosing also in the protected range of this patent in other situations.With average friendship and than (mIoU)
As evaluation index, spatial pyramid cover pondization can by PASCAL VOC (Everingham, M., Van Gool, L.,
Williams,C.K.I.,Winn,J.and Zisserman, A.International Journal of Computer
Vision, 88 (2), 303-338,2010) test set performance comparison such as the following table 1:
Table 1
Spatial pyramid cover pond is obvious to the promotion of performance, and accuracy rate alreadys exceed PASCAL VOC submission
The full monitor model FCN-8s of list.Spatial pyramid cover pondization and global pool metaplasia at prediction segmentation result comparison such as Fig. 4.
Multiple groups test comparative descriptions spatial pyramid and cover pondization with Small object identification, semantic relation correction and fringe region optimization
Ability.In conclusion it is to have for the improvement that Weakly supervised image, semantic is divided that spatial pyramid proposed by the present invention, which covers pond,
Effect.
Claims (7)
1. a kind of Weakly supervised image, semantic dividing method for covering pond based on spatial pyramid, comprising the following steps:
Step 1: selecting a convolutional neural networks H, handles input picture X by convolutional neural networks H, obtains characteristic of division
Figure;
Step 2: establishing spatial pyramid pond module according to characteristic of division figure, then carries out spatial pyramid cover, obtains defeated
Characteristic pattern out;
Step 3: calculating classification according to output characteristic pattern and activate vector sum class probability vector, then establishes competitive space gold
Word tower covers pond loss function;
Step 4: covering pond loss function training convolutional neural networks H according to competitive spatial pyramid and extracts segmentation spy
Sign figure.
2. a kind of Weakly supervised image, semantic dividing method that pond is covered based on spatial pyramid according to claim 1,
It is characterized in that the step of handling input picture X by convolutional neural networks H in the step 1 specifically:
Step 101: selecting a convolutional neural networks H, map to obtain segmentation characteristic pattern by the convolutional neural networks HWherein, C is target category quantity, and W is weight parameter, For real number field;
Step 102: characteristic pattern dimensionality reduction will be divided by 1 × 1 convolution, obtain characteristic of division figureCalculation formula
Are as follows:
Fcls=Fseg×W+b
Wherein, b is biasing,
3. a kind of Weakly supervised image, semantic dividing method that pond is covered based on spatial pyramid according to claim 1,
It is characterized in that the step two specifically:
Step 201: average pond being carried out to all characteristic of division figures, while specifying total pyramid number and pond core size, is obtained
To corresponding pyramid pond characteristic pattern Pi, wherein i be pyramid serial number, value range between 1 to total pyramid number it
Between, calculate i-th of pyramid pond characteristic patternCalculation formula are as follows:
Wherein, kiFor i-th of pyramidal pond core size, x is characterized figure abscissa, and y is characterized figure ordinate, △ kxFor pond
Change core abscissa, △ kyFor pond core ordinate, c is semantic classes quantity;
Step 202: each pyramid pond characteristic pattern being generated and covers tensorIt concurrently sets region and covers ratio
Example and cover quantization parameter;
Step 203: according to the output characteristic pattern for covering tensor computation spatial pyramid cover pondIt calculates public
Formula are as follows:
Oi=Pi⊙Mi
Wherein, ⊙ indicates hadamard product.
4. a kind of Weakly supervised image, semantic dividing method that pond is covered based on spatial pyramid according to claim 3,
It is characterized in that remembering that i-th of pyramid covers tensor and be in the step 202Remember that jth pyramid-like is covered
Tensor isIt is consistent with quantization parameter is covered that position is covered in the region that the different classes of pyramid covers tensor:
The ratio that the cover region generated at random accounts for the pyramid pond number of regions is τ,
Wherein, the value range of τ is [0,1], and the value of τ is bigger, and the region for indicating to cover is more,It indicates (1- τ)
HiThe value that W is obtained is rounded downwards, and I () indicates that indicator function, I () take 1 when input condition meets, take 0 in other situations,
I.e.The position of satisfaction is to be retained region, and other situations are the region covered;
The cover quantization parameter ρ is for inhibiting signal;
Wherein, the value range of ρ is [0,1].
5. a kind of Weakly supervised image, semantic dividing method that pond is covered based on spatial pyramid according to claim 1,
It is characterized in that calculating classification in the step 3 activates vector sum class probability vector specifically:
Step 301: calculating classification and activate vector oi, i.e., the output characteristic pattern that spatial pyramid covers pond is classified as a value, counted
Calculate formula are as follows:
Step 302: class probability vector being calculated by Softmax function, obtains the probability value of each semantic classes to be determined, is counted
Calculate formula are as follows:
6. a kind of Weakly supervised image, semantic dividing method that pond is covered based on spatial pyramid according to claim 1,
It is characterized in that in the step 3, establish competitive spatial pyramid cover pond loss function specifically includes the following steps:
Step 303: calculating the Classification Loss l of each pyramid gradei, calculation formula are as follows:
Wherein,Taking 1 other places to take 0, T for image, semantic class label vector and at the corresponding semantic classes target of appearance is to turn
Set symbol;
Step 304: the 0th grade of global pool information being fully retained, other pyramid grades for having carried out information cover are moved
State competition, calculation formula are as follows:
Wherein, lclsFor loss function of always classifying, N is pyramid number of levels, l0For the loss of global pool pyramid grade;
7. a kind of Weakly supervised image, semantic dividing method that pond is covered based on spatial pyramid according to claim 1,
It is characterized in that the step four specifically: calculate error with loss function of always classifying and adjust net by back-propagation algorithm
Network parameter is after the opposite variation of the loss function is less than 5%, directly by FsegIt takes out and takes argmax that can obtain in classification dimension
To prediction segmentation figure
Y=argmax (Fseg)
Wherein, argmax FsegThe 3rd dimension.
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